Dynamic spectrum management is a key technology in cognitive wireless sensor network (C-WSN), in which spectrum sensing plays an important role. In this paper, we propose an improved approach as sparse learning via iterative minimization based on compressive sampling (CS-SLIM) for wideband spectrum detection. CS-SLIM can provide wideband spectrum detection with almost the same accuracy but a lower computational burden than that of SLIM. The measurement matrix and the computational complexity for CS-SLIM are discussed. Mean squared errors (MSEs) at various measurement samples are provided to demonstrate the performance of the proposed approach in sparse scenes. It is also proved that the algorithm is suitable for sparse signal reconstruction and wideband spectrum sensing in C-WSN.